Top Artificial Intelligence Jobs

Engineer, operate, secure, and govern enterprise AI and automation platforms across development and production environments. Responsibilities include PostgreSQL and Redis administration, Linux and container infrastructure, high availability and disaster recovery, observability, incident response, CI/CD, Terraform-based infrastructure-as-code, identity integration, RBAC, security controls, audit support, documentation, and mentoring.

Leads pre-sales consulting for strategic Big Tech and AI customers, including discovery, solution design, demonstrations, architecture discussions, proof-of-concepts, executive presentations, and technical sales deliverables. Partners with Sales, Product, Engineering, Data Science, and post-sales teams to address identity, fraud, risk, compliance, and data requirements. Translates complex technical and analytical concepts into business recommendations, develops technical win strategies, mentors teammates, and ensures effective implementation handoffs.

Serve as a pre-sales technical advisor for strategic enterprise accounts adopting Claude. Partner with account executives to understand requirements, architect scalable LLM solutions, conduct evaluations, build prototypes, lead technical workshops, and guide customers from discovery through deployment. Develop executive champions and adoption roadmaps, communicate with technical and business stakeholders, influence product roadmaps, and help customers integrate Claude into existing technology stacks while maintaining safety and reliability.

Design and deploy scalable AI inference architectures for strategic partners. Build and operate GPU inference recipes, optimize pipelines using leading inference backends, and guide disaggregated serving deployments. Evangelize Kubernetes, compute fabric, DevOps, and observability practices while mentoring customers and internal teams. Lead technical engagements, resolve complex infrastructure issues, create reference architectures, and provide product insights. The role also involves agent design, sandboxing, memory and retrieval systems, evaluation, skill governance, and model customization.

Leads the data and AI strategy for post-purchase and innovation initiatives spanning fulfillment, returns, customer support, experimentation, and emerging marketplace channels. Builds and manages cross-functional data science, analytics, and data product teams; partners with executives, Product, Engineering, and business leaders on investment and roadmap decisions. Oversees experimentation, advanced analytics, algorithms, and scalable AI models while translating technical insights into measurable business outcomes.

Lead end-to-end design of AI-enabled networking experiences: convert ambiguous AI opportunities into product concepts, prototypes, and shipped features. Define AI-first interactions, data and telemetry requirements, reusable UX patterns for control and explainability, and partner with platform, engineering, and research teams to deliver trustworthy, measurable intelligence features.

Build, deploy, support, and scale AI-enabled enterprise applications, automations, integrations, and internal tools. Translate business workflows into governed production solutions, establish reusable deployment and support patterns, and assist citizen developers. Collaborate across IT, manufacturing, engineering, quality, supply chain, finance, HR, and other functions on testing, security, documentation, adoption, monitoring, and handoff. Measure business value, troubleshoot applications, and guide responsible AI use.

Own Arize’s product documentation and build automation to keep content accurate, detect code-documentation drift, test examples, and draft updates. Create tutorials, cookbooks, onboarding materials, and open-source demonstrations for developers and coding agents. Coordinate documentation with partners, frameworks, and course authors; gather community feedback; support marketing content; engage developers at events and online; and prototype product features with the Product team.

Assess and mitigate enterprise privacy risks across systems, cloud services, vendors, data-sharing arrangements, and AI technologies. Conduct PIAs and DPIAs, review DPAs and privacy contract terms, support incident investigations, apply global privacy laws, and contribute to responsible AI governance, risk assessments, vendor evaluations, policies, training, and documentation. Partner with Legal, Security, IT, Product, and business stakeholders.

Owns AI-enabled products from discovery through governed release, adoption, and continuous improvement. Translates banking business problems into product requirements, user stories, backlogs, delivery plans, controls, and measurable outcomes. Configures AI agents, retrieval patterns, workflow automations, data flows, and integrations while coordinating business, technology, data, risk, compliance, and operations partners. Monitors product usage, quality, business impact, adoption, and governance measures in a regulated environment.

Design, develop, and deploy enterprise-scale AI systems powering intelligent agents, copilots, and autonomous workflows. Build production-grade LLM, RAG, vector database, and agent orchestration solutions; develop APIs, SDKs, and platform services; and optimize reliability, latency, security, cost, and scalability. Lead initiatives through production, implement testing, CI/CD, monitoring, and governance, troubleshoot distributed systems, collaborate cross-functionally, contribute to architecture and documentation, and mentor or guide team members.

Leads Genesys’ AI product marketing strategy, including positioning, messaging, competitive differentiation, go-to-market programs, launches, customer proof, analyst engagement, and seller enablement. Partners with Product, Engineering, Sales, Customer Success, and Marketing to translate complex AI capabilities into compelling enterprise value. Represents Genesys externally as an AI thought leader and builds and develops a high-performing product marketing team while measuring impact across pipeline, adoption, seller effectiveness, competitive wins, and market perception.

Own day-to-day quality execution for AI data center equipment, including incoming, in-process, final, and electrical inspections. Manage supplier qualification, audits, corrective actions, FAI documentation, compliance matrices, test records, and Certificates of Conformance. Lead root-cause analysis, quality KPI tracking, customer complaint investigations, warranty support, and continuous improvement across chillers, e-houses, switchgear, and AI pods.

Assist the AI/ML lead in developing and refining retrieval and language-model systems for an intelligence and security research project. Responsibilities include building and evaluating retrieval systems, training or fine-tuning models, attending project meetings, documenting technical discussions, troubleshooting with an interdisciplinary team, and maintaining modular, documented code. The role requires Python, Linux, Git, machine-learning toolkit familiarity, collaboration, and eligibility to obtain a Secret security clearance.

Leads the transformation of Sales Operations into an AI-first organization by defining strategy, identifying automation opportunities, redesigning workflows, and architecting AI solutions across CRM, data, and enterprise platforms. Designs agents, RAG systems, and intelligent workflows; partners with Engineering and IT on integrations, governance, security, and scalability; establishes evaluation and observability standards; and drives enterprise adoption and executive alignment.

Own the design and implementation of WISEcode’s automated quality engineering ecosystem. Build AI-augmented UI, API, integration, regression, database, performance, and reliability testing frameworks for ASP.NET and Blazor applications. Integrate testing into CI/CD, improve observability and release confidence, investigate failures and race conditions, validate AI/ML outputs, and establish scalable quality standards and metrics across engineering teams.

Lead AI workload characterization and storage architecture for large-scale inference, RAG, and training systems. Analyze performance across inference runtimes, Linux storage and networking, SSD hardware, and GPU infrastructure. Drive data-placement strategies, modeling, benchmarking, customer proofs of concept, standards engagement, cross-functional architecture decisions, and technical mentorship. The role focuses on integrating AI data paths with NAND, SSD, NVMe, and networked storage systems.

Own strategic technology partner relationships and develop joint business plans, go-to-market motions, co-marketing campaigns, and sales plays. Drive partner-sourced and partner-influenced pipeline, revenue, and product adoption through account alignment and complex deal support. Influence product integrations and roadmap priorities, enable sales teams, engage partner executives, and coordinate with Marketing, Finance, RevOps, Product, and Engineering. Establish metrics, reporting, partner cadences, and repeatable engagement models.

Designs and delivers enterprise-grade AI-first full stack applications for Master Data Management. Develops Java backends, React or Angular frontends, REST APIs, microservices, data models, and integrations. Leads architecture, debugging, testing, performance, scalability, code reviews, Agile ceremonies, and technical direction. Uses and evaluates AI coding tools, mentors junior engineers, and collaborates with product, UX, QA, and platform teams.

Design, prototype, deploy, and maintain AI-powered applications and services. Build agentic workflows with orchestration frameworks such as LangGraph, apply prompt engineering and RAG, prepare multimodal datasets, evaluate AI models, and select tools for specific use cases. Develop web applications using Next.js, React, and Python frameworks while integrating AI capabilities with existing systems and workflows.

Owns enterprise AI architecture across AWS, Azure, Microsoft AI surfaces, and SaaS platforms. Establishes target architectures, standards, governance, security, responsible AI, and regulatory controls. Provides architectural authority for AI initiatives, guides platform and domain teams, evaluates emerging technologies, supports architecture review forums, mentors senior technical staff, and ensures production GenAI systems generate runtime model-risk and regulatory evidence.

Build and ship production machine learning systems for content abuse detection, including data pipelines, classifiers, LLM evaluations, inference infrastructure, monitoring, and model evaluation. Optimize precision, recall, latency, cost, and throughput while partnering with engineering and data teams. Develop moderator intelligence features, build training and serving platforms, establish metrics infrastructure, and mentor teammates. The role requires end-to-end ownership in a startup environment, from messy data and feature engineering through deployment and production monitoring.

Design, build, and deploy secure production AI applications using LLMs, RAG, agentic workflows, external tools, and enterprise data. Develop reusable orchestration, retrieval, evaluation, and observability components; create data pipelines; optimize reliability, cost, latency, and scalability; and deploy cloud-native services on AWS. Collaborate with multidisciplinary teams and clients to prototype, evaluate, and transition mission-critical AI solutions into production.

Lead enterprise-wide AI learning strategy and programs at Marsh: design, implement, and measure AI literacy and skill-building initiatives; partner with leaders and technical teams; build scalable, persona-based training; embed learning into work; track adoption and ROI; enable leaders and cultivate an AI learning community. Hybrid role based in New York.

Build and optimize high-performance LLM inference systems across data centers and edge devices. Develop inference engines, accelerate models on GPUs and AI hardware, design Kubernetes-based autoscaling architectures, and implement observability, load testing, and reliability frameworks. Improve latency, throughput, cost efficiency, and scalability across real-time and batch AI workloads while collaborating with hardware and cross-functional engineering teams.